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Block information strategy for multi-modal remote sensing image registration

Engineering Applications of Artificial Intelligence 2025年 157卷

作者： Hong, Yameng Leng, Chengcai Liu, Beihua Peng, Jinye Cheng, Irene Basu, Anup School of Mathematics Northwest University China School of Information Science and Technology Northwest University China Department of Computing Science University of Alberta Edmonton Canada

Registration of multi-modal remote sensing image pairs (MRSI) is challenging given the distinct imaging mechanisms of multi-modal data sources, which lead to substantial geometric and radiometric distortions and inaccuracies in correspondences. To tackle this issue, we propose a novel approach that integrates local image information into feature representations through the design of local regions and the extraction of local information. The latter comprises of two key components: rank-based feature redistribution and residual information extraction utilizing a pyramid-like structure of local patches. This enhanced feature representation technique, termed Reinforced Local information of LSS (RLILSS), embeds local information to improve the performance of the Local Self-Similarity (LSS)-based framework for MRSI registration. RLILSS strengthens feature characterization across various regions and addresses the limitations of supplementary information. This enables more reliable correspondences between images. Experimental results show that the proposed method achieves higher accuracy and better registration across diverse multi-modal datasets. Detailed analyses confirm its superiority over state-of-the-art methods in both accuracy and robustness. This approach holds significant potential for applications in automatic geographic registration and disaster area reconstruction. © 2025 Elsevier Ltd

关键词： Modal analysis

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OIDCBMS: A Novel Neural Network based Intrusion Detection System to Cloud computing based on New Cube Algorithm 4

OIDCBMS: A Novel Neural Network based Intrusion Detection Sy...

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4th International Conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Srilatha, Doddi Thillaiarasu, N. School of Computing and Information Technology Reva University Bengaluru560064 India Sreenidhi Institute of Science and Technology Department of Cse Hyderabad501301 India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Intrusion Detection System (IDS) is a significant segment for maintaining network security. Likewise, as the cloud stage is rapidly developing and becoming more famous in our daily existence, it is valuable and important to construct an effective IDS for cloud computing. Notwithstanding, present intrusion detection procedures will probably confront difficulties when deployed on the cloud stage. The pre-decided IDS engineering may prompt over-burdening of a piece of the cloud because of the additional discovery overhead. The presented approach, Online Instruction Detection Cloud-Based Management System (OIDCBMS), receives the novel NN (Neural Network) design standards termed as New Cube algorithm. It is the primary sorting-out framework approach that uses the New Cube calculation. The OIDCBMS takes over the hybrid learning aspect of the New Cube algorithm and utilizes such calculation in an online framework by deep-rooted figuring out how to group input while learning the framework. In addition, with the AI capacity with the neural organization, the projected IDS may identify novel kinds of attacks with genuinely precise outcomes. Using zero-day assaults, the framework's precision increases to roughly 096.74 percent depending on the to-be-remembered (TBR) encoding computation. © 2022 IEEE.

关键词： Intrusion detection

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Enhanced Feature Selection for Superior Ensemble DDoS Detection in Software-Defined Networks

Enhanced Feature Selection for Superior Ensemble DDoS Detect...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Alashhab, Abdussalam Ahmed Edrah, Aisha Abbas, Maythem K. Alashhab, Mohamed Alngib, Mohamed Daha, Muhammad Yunis Alasmarya Islamic University Faculty of Information Technology Department of Computer Science Zliten Libya Western University Faculty of Engineering Department of Electrical and Computer Engineering London Canada Asia Pacific University of Technology and Innovation School of Computing Kuala Lumpur Malaysia Universiti Sultan Azlan Shah Faculty of Management and Information Technology Kuala Kangsar Malaysia School of Engineering Ulster University Belfast United Kingdom

ISBN: (纸本)9798350391213

Distributed Denial-of-Service (DDoS) attacks disrupt networks by flooding systems with traffic from multiple sources, making real-time detection essential. Integrating Machine Learning (ML) with Software-Defined Networking (SDN) presents a viable solution;however, existing ML methods often face challenges such as high false positive rates and difficulty detecting sophisticated attacks like low-rate and zero-day DDoS attacks due to inadequate training features. As DDoS attack methods evolve, continuous retraining of ML models becomes necessary. This paper introduces a feature selection approach for ensemble classifiers to improve DDoS detection in SDNs. Through correlation analysis and principal component analysis (PCA), the most relevant features are identified, leading to substantial improvements in model performance. Our evaluation, using diverse DDoS datasets, demonstrates that the proposed method achieves near-perfect accuracy, a 100% true positive rate, and minimal error rates, offering a highly effective solution for DDoS detection in SDNs. © 2024 IEEE.

关键词： Zero-day attack

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An Implementation of Photovoltaic Solar Panel Monitoring System Based on Internet of Things

An Implementation of Photovoltaic Solar Panel Monitoring Sys...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： J. Saranya V. Divya School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The Internet of Things (IoT) is a contemporary technology that enhances user experience and adds intelligence to objects. Numerous applications have already embraced the Internet of Things. The Internet of Things (IoT) has greatly simplified and increased the accessibility of our lives. Society will be better equipped to safeguard future generations by reducing greenhouse gas emissions and ozone depletion as long as everyone uses renewable energy. These PV technologies are growing in popularity because they are so widely available, reasonably priced, and easy to install and maintain. Monitoring is required to determine the current condition of each solar photovoltaic cell in a solar panel. This monitoring is necessary to detect any defects in the solar cell and implement repair actions in addition to guaranteeing that the solar panel continues to function properly. In this study, an Internet of Things-based solar panel monitoring system is implemented, and the MPPT algorithm is used to track maximum power.

关键词： Wireless communication Maximum power point trackers Renewable energy sources Photovoltaic cells Energy efficiency User experience Internet of Things

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A comparative study of various techniques for crop Disease Detection and segmentation 4

A comparative study of various techniques for crop Disease D...

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4th International Conference on Smart Systems and Inventive technology, ICSSIT 2022

作者： Prakash, Varun Singh, Sanjay Aggarwal, Ankur Kumar Raghav, Sagar Sood, Shubham Pandian, M. Thurai Dept. of Computer Science and Technology Manav Rachna University Faridabad India School of Computing and Information Technology REVA University Bengaluru India

ISBN: (纸本)9781665401180

Plant diseases have always been a major concern with food security in particular, the quality assurance of the agricultural products usually depends on the appropriate identification of the diseases, if any, in the crops used in their production. Moreover, since a vast majority of world's economy usually depends on the agriculture, it becomes more important to cut short on the losses incurred with the traditional methods of crop disease detection that involves human supervision and observations taken with naked eyes which are often inaccurate, time consuming and error prone. Since food production and its consumption could have consequence that spans multiple spheres ranging from a country's economic growth to health of its denizen, it becomes more than relevant to establish a rather reliable and automatic process for crop disease detection. This study proposes a comparative study underlying the various researches involving computer vision in particular, Machine Learning and Deep Learning, for crop disease detection automation, as a part of underlying framework, this study intends to present the necessary methods and techniques such as acquiring data, feature extraction, image segmentation, etc., that withstood the test of the time in the required context. © 2022 IEEE

关键词： Computer vision

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A probabilistic public key encryption switching scheme for secure cloud storage

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International Journal of information technology (Singapore) 2023年第2期15卷 675-690页

作者： Bhat, Radhakrishna Sunitha, N.R. Iyengar, S.S. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur 572103 India School of Computing and Information Science Florida International University Miami FL United States

The high demand for user-centric applications such as secure cloud storage laid the foundation for the development of user-centric security schemes with multiple security features in recent years. But, the current state-of-art techniques primarily emphasized only one type of security feature i.e., either homomorphism or non-malleability. To fill this gap and provide a common platform for both homomorphic and non-malleable cloud applications, we have introduced a new public key-based probabilistic encryption switching method. The proposed scheme generates O(m+2 log N) bits of ciphertext where m∈ N and m< n, n∈ N is the plaintext size, N is the RSA composite. The superior performance of the proposed scheme has been tested in comparison to existing methods and is reported in this paper. © 2022, The Author(s).

关键词： Encryption Switching Homomorphic encryption Probabilistic encryption Public key cryptosystem Secure Cloud storage and retrieval

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Automate Threat Detection and Analysis Through Intelligent Data Mining Techniques for Network Traffic and Cybersecurity 1

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2nd International Conference on Cyber Intelligence and information Retrieval, CIIR 2023

作者： Muloor, Kiran Hemanthraj Sahu, Somesh Samanta, Debabrata Karuppiah, Marimuthu Bhattacharya, Abhishek Dutta, Soumi LTIMindtree Limited Karnataka Bangalore India Department of Computer Science CHRIST University Karnataka Bangalore India Department of Computing and Information Technologies Rochester Institute of Technology Pristina Kosovo School of Computer Science and Engineering and Information Science Presidency University Karnataka Bengaluru560064 India Sister Nivedita University Kolkata India

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

Today, we are constantly surrounded by vast amounts of data, a trend that is expected to grow significantly over the next decade. The abundance of data presents challenges for thorough analysis and extraction of valuable insights buried within unstructured information. Advanced tools like data mining are crucial in uncovering this useful information and making full use of it. In light of the increasing number of security threats in networks, there is a need for robust security solutions. While traditional network security measures have been primarily managed locally, concerns about internet-based security have grown due to heightened computer usage leading to cybercriminal activities previously limited to physical intrusions. A threat intelligence program aims to enhance analytical and preventive capabilities by acquiring knowledge about potential or existing threats based on evidence. As most devices are interconnected with the Internet, many organizations prioritize cybersecurity as they acknowledge the vulnerabilities arising from this connectivity—providing opportunities for cyber-attacks. Effective threat intelligence concerning network traffic necessitates a comprehensive understanding supported by thoughtful representation techniques. This paper proposes an extensive exploration of various machine learning methods aimed at identifying weaknesses in detecting invasive activity using different approaches and evaluating their performance against the KDD 99 benchmark dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Diagnosis of Covid-19 from Chest X-Ray Images Using Wavelet-Based Depth Wise Convolution Network 3

Diagnosis of Covid-19 from Chest X-Ray Images Using Wavelet-...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Tarun, Sai Rakesh, Sai Safa, M. Saranya, G. Chakrapani, K. SRM Institute of Science and Technology Department of Networking and Communication Chennai India School of Computing SASTRA DEEMED University Department of Information Technology India

ISBN: (纸本)9781665462754

There is a great need to create and put in place a method of automatic detection as a substitute for conventional diagnosis for COVID-19 detection that can be employed on a commercialscale because there aren't as many COVID-19 test kits availablein medical institutions. In particular, chest X-Ray scans can beexamined to assess whether a patient has COVID. Due to the availability of numerous big annotated picture datasets, convolutional neural networks have achieved remarkable success in image analysis and classification. Input is obtained in the form of chest x-rays images. Output results are acquired instantly in real-time which predicts if the person suffers from Covid or not. Modern technique use the RCNN algorithm, which makes them less precise and time-consuming. We suggest an automated deep learning-base method for extracting COVID-19 from chest X-ray pictures. For analysing the chest X-Ray pictures, suggested method offers enhanced depth-wise convolution neural network. Through wavelet decomposition, multiresolution analysis is incorporatedinto the network. In order to identify the condition, the network is given the frequency sub-bands that were recovered from the input pictures. The network's goal is to determine whether the input image belongs to the Covid-19 class or not. The Advantage of the proposed system are that it could be the very first-of its kind, cost-efficient, and highly accurate application that provide complete and accurate covid - 19 diagnosis. © 2022 IEEE.

关键词： COVID-19

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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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Computer Modeling in Engineering & sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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